Big Data Innovation, Issue 22

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BIG DATA INNOVATION MAY 2016 | #22

Achieving a SPORTS Data-Driven PERFORMANCE Culture & TECH T H E

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AUG 2015 | #14

Ethereum- The Future Of The Internet Or A Bitcoin Pretender?

What’s Changed Since The 2011 McKinsey Big Data Report?

As digital currency is hitting the

The report outlined how it thought the

Theadlines, H E weL put E the A D I N G spotlight on

Ethereum a potential bitcoin rival | 6

V Oindustry I C EwouldO F P & A mature, 5Fyears on we take a look at its accuracy | 24

Data Visualization Summit September 8 & 9, 2016 | Boston

Speakers Include

+1 415 614 4191 jc@theiegroup.com www.theinnovationenterprise.com

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ISSUE 20

EDITOR’S LETTER Welcome to the 22nd Edition of the Big Data Innovation Magazine

The world today is a very different place from only 10 years ago.

We now see driverless cars as a viable technology, physical media has been replaced by a screen and the world has gone through one of the deepest recessions in its history. Another key element that has changed considerably is how people have begun to shift towards the right hand side of the political spectrum, moving towards protectionism rather than collaboration. We have seen with Donald Trump’s rhetoric about walls and the UK’s much discussed ‘Brexit’ that this is a theme across the western world.

the US making several headlines following a court decision that nullified the original system.

If the world becomes a more isolated place, the data that we share will become far more difficult to use, not just for companies to collect, but will potentially have even larger consequences on the ability to collaborate in other areas.

The question that we need to ask is how much this could potentially impact those who work with data and what would be the likely challenges we face in data collection?

Concentrating on the EU debate (as this has more evidence behind it), the EU is currently the global leader in terms of scientific researchers, housing 22.2% of the global demographic. One of the great things about the current EU system is that this huge number of scientists can move between countries to collaborate and create data with ease. It means that data created in Switzerland one day can be tested in the UK the next.

We have already seen that data sharing across borders is incredibly important, with the issues surrounding the collaboration agreements between the EU and

In fact, when you look at the number of scientific papers currently coauthored by the UK and international scientists in collaboration, it makes up over 50% of the total UK output.

This increase in research would not have been possible in an isolated country, as we would have seen significant delays in sharing data produced and a notable decrease in the number of people capable of effectively analyzing it.

Even more importantly, we would not have the kind of data talent in our countries. Imagine if DJ Patil’s parents would have never been able to move to the US? If the team who created Hadoop would never have been able to work together?

If Gregory Shapiro had not been offered a scholarship in the US? International collaboration and free movement have been at the heart of the rise of our data-driven society, so if we move back to a state where this is not the case, the industry as a whole will suffer.

George Hill managing editor

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Boston Data Festival September 8 & 9 | 2016

SUMMITS Big Data Innovation Data Visualization Internet of Things

Speakers Include

+1 415 614 4191 jc@theiegroup.com www.theinnovationenterprise.com

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contents 6 | ACHIEVING A DATA-DRIVEN CULTURE

We take a look at some of the key elements that companies have used to create a culture based on data

16 | HOW BIG DATA IS USED TO HELP THE CIA AND TO DETECT BOMBS IN AFGHANISTAN

Bernard Marr discusses Palantir in this exclusive extract from his new book

10 | WHY OUR LEADERS NEED TO UNDERSTAND DATA

21 | HOW DO WE GET OUR KIDS INTERESTED IN TECH?

In an important year for politics across the world, we investigate why it is so important for leaders to know how to use data

Despite knowing exactly how to use technology, our kids are almost clueless as to how it actually works, how do we change that?

12 | BANKS ARE FAILING TO CAPITALIZE ON THE DATA REVOLUTION

24 | WHAT’S CHANGED SINCE THE 2011 MCKINSEY BIG DATA REPORT?

Despite having huge data potential, banks are lagging behind other industries on adoption of new data techniques

The report outlined how it thought the industry would mature, 5 years on we take a look at its accuracy

14 | ETHEREUM- THE FUTURE OF THE INTERNET OR A BITCOIN PRETENDER?

As digital currency is hitting the headlines, we put the spotlight on Ethereum a potential bitcoin rival WRITE FOR US

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managing editor george hill

| assistant editor james ovenden | creative director chelsea carpenter

contributors bernard marr, matthew reaney, alex lane

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Achieving A DataDriven Culture Mico Yuk is the author of Data Visualization for Dummies and founder of BI Brainz and the BI Dashboard Formula (BIDF) methodology. She has trained thousands globally how to strategically use the power of data visualization to enhance the decisionmaking process, working with Fortune 500 companies including Procter & Gamble, Honda, Kimberly-Clark, Royal Dutch Shell, Nestle, Qatargas, Ericsson and FedEx. We sat down with her.

James Ovenden, Assistant Editor

How did you start your career in data?

Weirdly enough, I actually started my career in SaaS as a data scientist.

Data was my currency. Without data, you were dead in the water. My job was to create and run algorithms that delivered and proved the stats that were being published by Forbes about our high-profile college football team. A few years later, I entered the business intelligence world. What a big change! Everything in this world was Excel. No one had to prove their numbers. The realization that I had a /6

lot of ‘data discipline’ to bring to the table and the BI industry is how my love affair with data started.

How have you seen the industry change over the last decade?

In the last 10 years, technology has changed and business users’ expectations have changed.

However, the challenges have not. The prevalence of big data

more information. I hope to see this trend continue.

What do you see as the major challenges confronting the data viz project over the next few years, and what technologies do you think will be game changing?

coupled with disruptive tools such as Hadoop, IBM Watson Analytics, SAP HANA, Tableau, Qlikview and Domo are redefining the analytics industry. The traditional BI vendors are being forced to reinvent their old BI tools or become extinct.

The emergence of IoT has created an insatiable thirst for advanced analytics, which in turn has driven a demand for data scientists that the market can’t meet. Wearable and smartphone devices continue to shrink the consumer viewing real estate, forcing the display of billions of rows of data into a single number or chart. It’s both exciting and scary.

One thing that has not changed is the business vs IT challenge. The traditional data wars of ownership versus stewardship. Many BI teams are at risk for becoming ‘BI-nosaurs’ (per Gartner), as they struggle to deliver value to the business. Ten years ago, IT teams controlled the company’s technology spend.

Today most CIOs can’t secure a technology or resource budget without gaining extensive business review, buy-in and approval. This has meant that lots of technical people have basically become pseudo marketers. The second hottest addition to the C-level in most progressive companies, besides the Chief Digital Officer, is the Chief Analytics Officer (CAO).

The creation of the CAO role signals a data revolution, where users are demanding knowledge and not just

When used properly, a data visualization is the most powerful way to communicate data. Human being recall images 60,000 times more than they do text. However, most organizations face the same three challenges: 1) Data. Having the right data is going to be one of the key challenges, and the time it takes to validate the data. It is important to measure the right KPIs or metrics, as looking at the wrong ones leads to data visualizations that contain lots of information but no real action or insight, and ultimately a lack of user adoption.

2) Design. A lack of design and UX skills will also hold organizations back. Traditional data visualization thought leaders like Stephen Few, Rolf Hichert and others subscribe to the black and white, less is more approach. Today, users expect their business intelligence assets such as dashboard and reports to look, function and perform like the apps on their phones and desktops.

Users want personalized, user friendly, aesthetically appealing, and easy to understand data viz that provide clear actions.

3) Tools. Old BI tools vs new BI tools. One uses a ‘single version of the truth’ the other promotes data silos. The solution to these problems is not just better technology. In the last nine years of building the BI Dashboard Formula (BIDF) methodology, we realized quickly that the biggest problems reside with the people who were

using the technology. That is why BIDF techniques are 50% right brain (soft skills), 50% left brain (analytical skills). We provide tools and strategies that facilitate clear communication.

How important do you think it is for an organization to build a data-driven culture? To what extent do you think data visualization enables them?

‘In God we trust, everyone else brings data.’

- William Edwards Deming -- a renowned American statistician, professor, author, lecturer, and consultant.

Although that saying is from the 1950’s, it still holds true today.

Data-driven cultures are a must for large and small organizations. The rise of big data and ‘other’ types of useful data marked the death of ‘gut analysis’ (the number feels good analysis).

For organizations to take advantage of the opportunities that IoT and big data present, companies must not only promote but also reward data-driven individuals. Insight cannot be gained by asking a single question, true insight is learning the question you don’t know to ask.

Organizations need their data to become intelligent, and they can only do so by empowering the individuals that own it. Data-driven cultures that are evolving go from reporting data to telling visual data stories that inspire and invoke action. This is where the BI Dashboard Formula methodology can help.

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How to do you go about creating a data culture? Is it better to start from the top down, or work bottom up?

makes a great data ‘Without big data analytics, What visualization? companies are blind and ‘The greatest value of a deaf, wandering out onto picture is when it forces ‘ Without data, you’re the Web like deer on a us to notice what we never simply another person with freeway.’ expected to see.’ an opinion.’

- Geoffrey Moore, Author of Crossing - Jonah Harris This is the message that the leaders for data-driven cultures have to embed in the DNA of their organizations. Creating a data culture must start from the top, with leaders who demonstrate the importance of data-driven decisions. Leaders should start by first presenting and demanding data for all decisions, then encourage all managers to do the same.

Leaders must also set examples, by rewarding data-driven users whose actions provide a measurable impact to the bottom line. Data source and hygiene should become the number one priority.

A recent Business2community survey of data professionals found that the data science skill with the highest correlation to project success was data mining and visualization tools. Do you agree with this?

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the Chasm & Inside the Tornado

To the average person, data scientists are geeks who take data and build monster algorithms to find little nuggets a company can use to understand the most complex problems. What is often forgotten is that once those nuggets are found, the main value of data scientists is their ability to tell a story with that data that the anyone can understand.

The data mining part is not surprising, but I know that for many in the analytics and business intelligence industry, they don’t realize that the only way for a data scientist to communicate their findings with the average Joe Some (non-PhD) is by using simple visuals and telling stories. Otherwise, their insight is useless to a company.

- John Tukey, American Mathematician

Great data visualizations tell stories.

Not just pretty stories, but stories that provide insight, outcomes, and actions. Telling a good story is not complex, but the simplicity is where most fail. A great story does not tell you everything, just the things you need to know. Great visualizations don’t need to be explained. There are many schools of thought that focus on the design elements of great data visualizations such as Edward Tufte, Stephen Few and Rolfe Hichert.

In my BI Dashboard Formula, we focus more on telling the story.

A good story doesn’t need to be pretty, it just needs to simple and useful.

You can find out how to apply Mico’s methods to your own organization with her online course, which is now open for enrolment.

Premium online courses delivered by industry experts

academy.theinnovationenterprise.com

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Why Our

Leaders Need To Understand Data George Hill, Editor-In-Chief

April 26 2016 marked one of the saddest days in the history of the NHS in the UK, as for the first time there was a full strike from the service’s junior doctors (every doctor under consultant rank).

The reason for April 26’s strike is because UK health secretary, Jeremy Hunt, is attempting to impose a new contract on them to create a new ‘7 day NHS’.

Although there has been widespread support for the doctors across the UK, one of the key points undermining Jeremy Hunt’s position is that he has little to no data to back up his new contract and the claims about why it is needed. For instance, his main argument is that he needs to prevent the ‘6,000 yearly deaths’ caused by weekend staffing issues in the NHS, but the author of the study that he quotes has himself said that this data is wrong.

Even the day before these historical strikes, he was roundly criticized for not adopting a pilot scheme to try out the contract in some hospitals.

Rather than gathering data on the impacts that it would have in the long run, he attempted to make a political statement.

The case for whether the new junior doctor’s contract is a good or bad idea is one thing, but the ignorance / 10

of the data surrounding the issue is the most damning indictment of his leadership. In the same way, that analysis and modelling hasn’t been done to at least attempt to see the impact in future, the minister’s reliance on false data is troubling.

Data is everywhere in our society, and people do not want to have decisions made that will undoubtedly have a huge impact on their lives without data to back it up.

It is not simply in the decisions that our leaders make with data, but also the importance that people place on their own data. Governmental spying on people’s personal data has done substantial damage to the relationship between citizens and governments across the world.

Those who did the spying have been chastised and those who allowed it to happen have been equally impacted. It has even seen decades-old data treaties destroyed through citizen court action, like the SafeHarbor principles which were deemed illegal following Edward Snowden’s revelations.

Both of these examples show that not only do country leaders need to keep on top of data, but that the consequences of not doing so can be severe. In fact, it could be argued that a lack of concentration on data has, in no small part, caused the current discontent in many western political systems.

Using data correctly has a significant positive impact too. It is not just used to reinforce arguments, rather it can even start to heal the rifts that have opened between governments and those who feel particularly disenfranchised. We saw how Barack Obama managed to use data to effectively create a personalized election campaign, and he subsequently managed to secure the 6th largest winning margin in US popular voting history.

It shows that, with effective use of data, it is possible to create far more robust and achievable leadership techniques to keep the people they represent happy and the policies they create effective. We have seen through analysis that things like extended periods of austerity do little to increase public happiness and can have a big impact on public services, further damaging leadership viability in the future.

One of the key points undermining Jeremy Hunt’s position is that he has little to no data to back up his new contract and the claims about why it is needed

Not just understanding data, but basing policies and decisions on sound and wide ranging data sets makes for better leadership; we have seen the impact it can have when people ignore it.

Going back to the NHS, we have seen how those against the new contract being imposed by Jeremy Hunt have cleverly used data to show that women would be adversely, and disproportionately, affected by it. In fact, this could potentially stop the entire process in its tracks, as it has formed the basis of a legal challenge against the contract given the potential discrimination against women. It has created an irrefutable argument against the contract and has been picked up by millions of pro-NHS campaigners across the UK.

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Alex Lane, Data Commentator

Banks Are Failing To Capitalize On The Data Revolution

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BANKS HAVE ACCESS to an amount of consumer data that organizations in other industries would kill for. New digital platforms have meant an explosion in the amount of data available about people’s financial habits. The management of transactions and the nurturing of relationships over time have provided insights into customer behavior that has enabled them to maintain a competitive edge. Some banks have been mining, analyzing, and leveraging data for decades now. These banks are, however, in the minority, and most are nowhere near as mature as they should be in their data analytics capabilities.

What’s really surprising is that banks are still not prioritizing data analysis. Only the largest regional and national banks (over $10billion) rank improving data and analytics capabilities in their top three priorities (47%), and just 36% of organizations plan to increase their data analytics budgets by more than 10% in 2016. The data is there, the analytical models are there, and the talent to make the data meaningful is there, so excuses for not leveraging consumer insight is really difficult to justify. There are, however, a number of challenges unique to the banking sector that put them at a disadvantage.

This failure to keep up is having a hugely detrimental impact on their ability to operate successfully.

According to a new survey by business and IT services provider NTT Data Inc, one in three consumers would consider leaving their bank for a better online and mobile experience, while 71% of consumers think their bank could better support their banking needs.

The root cause of this is the neglect to analyze the data being generated, particularly that generated by new kinds of consumer-facing products, like apps.

Legacy systems and siloed data is the primary issue holding banks back. The influx of regulation and the cost of compliance has meant that, while many banks have invested heavily in front-end improvements, they are some way behind in improving the back end. The banking industry has far more problems with legacy data compared to other industries because of the complexities of their operations, and overcoming this has proved a laborious process for many.

One of the shining lights is JPMorgan, for example, which is investing heavily. JPMorgan CEO Jamie Dimon wrote recently that, ‘To best utilize our data assets and spur innovation, we have built our own extraordinary in-house big data capabilities - we think as good as any in Silicon Valley - populated with more than 200 analysts and data scientists, which we call Intelligent Solutions.’ He may be right, but, in general, banking is still struggling to steal the best technical graduates away from Silicon Valley. Put frankly, banking is not always viewed as the most exciting career opportunity for graduates, particularly compared to the young, vibrant tech sector.

Banks are trying to change this and adapt the culture to be more attractive. HSBC, for example, now has a separate office for tech staff that better suits the Silicon Valley mindset. There is still a stigma though, and they will struggle to overcome this.

The positive news is that money being spent on regulatory compliance is decreasing, which means funds are now available for data analytics initiatives. Banks are, however, going to have to start putting a greater emphasis on getting it right. FinTech firms are knocking at their door, and will exploit any gap they can find.

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Ethereum: The Future Of The Internet Or A Bitcoin Pretender?

DR CRAIG WRIGHT’S RECENT ‘REVELATION’ THAT HE IS SATOSHI NAKAMOTO, the secretive creator of the digital currency Bitcoin, has brought Bitcoin back into the public focus for the first time in a while.

James Ovenden, Assistant Editor

Whether Dr. Craig Wright’s claims are true or not, it doesn’t really change anything in practice other than satisfying the curiosity of a few, and providing a focal point for the hatred of those who believe him a liar. However, it comes at period of time when the importance of blockchain, the distributed ledger system that underpins Bitcoin, is starting to be realized, and will do it no harm in its quest to become the buzzword of 2016.

Bitcoin may be the most famous application for blockchain, but there are others making headway.

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Ethereum is one of these, gaining significant traction since its initial campaign in 2014. It is now the largest and most well-established, open-ended decentralized software platform, with a total market cap of $747,905,311 at last count.

Ethereum provides a digital currency token, known as an Ether, as an incentive to those who work on its peer-to-peer computation network to build out on its applicationfoundational blockchain technology, in the same way Bitcoin operates.

Where the two differ is that Ethereum was developed with a focus on smart contracts - computer

code or ‘scripts’ that facilitate, verify, and enforce the negotiation or performance of the terms of a given contract between a number of parties - while Bitcoin was developed to focus on a peer-topeer digital cash. Put simply, if Bitcoin is like email, Ethereum is like the internet.

Essentially, the objective of Ether is to facilitate and monetize the working of Ethereum to enable developers to build and run distributed applications. The main advantage of the smart contracts it uses is that it takes a lot of the risk out of owning Ether by defining a smart contract that holds the tokens, as opposed to a third party wallet service. Bitcoin does use smart contracts, but this usage is limited to honoring Bitcoin transactions. The emphasis on trust is why Ethereum is so important, particularly in the age of the sharing economy, when recommendation engines are so important.

Ethereum has big ambitions to be an enabler to the decentralized cyber economy, with many applications for services such as Uber. While Ethereum started out as a means to govern only financial application states, it has greatly expanded its remit into the non-financial space.

Essentially, according to core developer, Vitali Buterin, it wants to be the world computer - a magic computer in the Cloud that anyone can send and run programs to it, and those programs can talk to each other, and you can trust that you can run those programs. He uses the basic example of setting a reminder for something in five years time. You would create a program to remind you of something automatically in five years and put it on the world

computer, paying a transaction fee to do so, and could be guaranteed that this will happen and nothing could prevent it.

Another example of Ethereum in action would be allowing farmers to put their produce up for sale directly to consumers and take payment directly from consumers. Chronicled, a startup using blockchain technology to help authenticate collectible sneakers, has also developed pilot implementations on both Bitcoin and Ethereum blockchains, and said that it preferred the Ethereum framework and coding language to be the most user-friendly and flexible of the two.

This emphasis on trust has a number of advantages. For developers, it means greater cost savings and efficiency in writing new applications. For business users, it means a chance to completely redesign their existing business structure and create new opportunities, based on unbundling central functions and relegating them to decentralized constructs.

As Buterin notes, ‘Ethereum helps anyone wishing to develop decentralized applications, encode arbitrarily complex contractual business logic, launch autonomous agents, and manage relationships that will be mediated entirely by the blockchain.’

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HOW BIG DATA IS USED TO HELP THE CIA AND TO DETECT BOMBS IN AFGHANISTAN This article is an extract from Bernard Marr’s new book ‘Big Data In Practice:

‘How 45 Successful companies used big data analytics to deliver extraordinary results’ Bernard Marr, Data Author

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BACKGROUND PALANTIR, NAMED AFTER THE MAGICAL STONES IN THE LORD OF THE RINGS used for spying, have made a name for themselves using Big Data to solve security problems ranging from fraud to terrorism. Their systems were developed with funding from the CIA and are widely used by the US Government and their security agencies. Their annual revenue is reported to be in the region of $500 million and they are forecasted to grow even larger - at the time of writing (January 2016) the company are tipped to go public with an IPO and are currently valued at $20 billion.

WHAT PROBLEM IS BIG DATA HELPING TO SOLVE? Initially working on tools to spot fraudulent transactions made with credit cards, Palantir soon realized the same pattern-analysis methods could work for disrupting all forms of criminal activity, from terrorism to the international drug trade. Now, their sophisticated Big Data analytics technology is being used to crack down on crime and terrorism.

HOW IS BIG DATA USED IN PRACTICE? Palantir build platforms that integrate and manage huge datasets, which can then be analysed by their wide range of clients - including government agencies and the financial and pharmaceutical industries.

Much of their work is naturally veiled in secrecy, but it is widely known that their routines for spotting patterns and anomalies in data which indicate suspicious or fraudulent activity are derived from technology developed

by PayPal (Peter Thiel, who also cofounded the online payment service, is a Palantir co-founder).

They have been credited with revealing trends that have helped deal with the threat of IEDs (improvised explosive devices), suicide bombers in Syria and Pakistan and even infiltration of allied governments by spies. The US Government are Palantir’s biggest customer, and their software has become one of the most effective weapons in the digital front of the ‘war on terror’. Marines, for example, have used Palantir tools to analyse roadside bombs in Afghanistan and predict attacks and the placement of bombs.

The data needed to support Marines in Afghanistan was often spread across many sources without one / 17

The company were implicated in the WikiLeaks scandal, when they were named as one of three tech firms approached by lawyers on behalf of Bank of America seeking proposals for dealing with an expected release of sensitive information

single interface to access and analyse the data. Therefore, the United States Marine Corps (USMC) charged Palantir with developing a system that could integrate these sources quickly. The aim was to improve overall intelligence and reduce the amount of time spent looking for information. As units are often working in areas with low bandwidth or with no bandwidth at all, the system had to work without being connected to base stations.

The Palantir Forward system provided the answer to this problem, as it automatically synchronized data whenever the connection to base stations was restored. USMC analysts were able to use Palantir’s data integration, search, discovery and analytic technology to fuse the data and provide greater intelligence to Marines on the frontline.

A key philosophy of the company is that human intervention is still needed to get the most from data analysis ‘ particularly when you have to think one step ahead of an enemy. To this end, they provide handpicked expert consultants to work in the field alongside their clients on data projects.

WHAT WERE THE RESULTS? Using Palantir’s system, USMC analysts were able to detect correlations between weather data and IED attacks, and linked biometric data collected from IEDs to specific individuals and networks.

None of this would have been possible without having all the data integrated and synchronized in one place.

Palantir have now raised $1.5 billion in venture capital funding, indicating an enormous level of confidence in their technology.

And the power of their platforms is being recognized beyond the realm of law enforcement and defence; the company are attracting many

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corporate clients, such as Hershey’s, who are collaborating with Palantir on a data-sharing group.

WHAT DATA WAS USED? In the Afghanistan example, the data used included a wide range of structured and unstructured data: DNA databases, surveillance records showing movements, social media data, tip-offs from informants, sensor data, geographical data, weather data and biometric data from IEDs. A big part of Palantir’s success lies in pulling such massive data sets together effectively.

WHAT ARE THE TECHNICAL DETAILS? Palantir are understandably secretive about technical details, which means I am unable to share details on how data is stored or analysed.

ANY CHALLENGES THAT HAD TO BE OVERCOME? Privacy is a murky area in the Big Data world, and for companies such as Palantir that gather enormous amounts of data public perceptions surrounding their use of that data is bound to be a concern. The company were implicated in the WikiLeaks scandal, when they were named as one of three tech firms approached by lawyers on behalf of Bank of America seeking proposals for dealing with an expected release of sensitive information. After their name was linked to the scandal, Palantir issued an apology for their involvement.

Concerns are growing about government use of individuals’ data, particularly in the US and the UK, in the wake of the Edward Snowden NSA leaks. As such, Palantir need to tread a fine line between gathering the data necessary for

Marines, for example, have used Palantir tools to analyse roadside bombs in Afghanistan and predict attacks and the placement of bombs

the job at hand and avoiding mass invasion of privacy. It’s an issue that founder Alex Karp doesn’t shy away from. Speaking to Forbes a couple of years ago, he said: ‘I didn’t sign up for the government to know when I smoke a joint or have an affair.’ And in a company address he stated: ‘We have to find places that we protect away from government so that we can all be the unique and interesting and, in my case, somewhat deviant people we’d like to be.’1 With the company’s reported IPO coming up, public perception is likely to be as important as ever and it’ll be interesting to see how they manage this.

WHAT ARE THE KEY LEARNING POINTS & TAKEAWAYS? One of the key points that Palantir make is that human interaction with data is just as valuable as the data itself. This is true whether you’re fighting a war or trying to attract new customers to your product or service. There is a danger that we place too much blind faith in data itself, when, in fact, how we work with that data and make decisions based on it is the key.

Palantir also provide an excellent example of how data can be especially powerful when more than one dataset is combined. Working with just one dataset can provide a very one-sided view ‘ often it’s the correlations and interactions between different types of data that provide the real insight gems.

Sources REFERENCES AND FURTHER READING 1. Greenberg, A. (2013) How a ‘deviant’ philosopher built Palantir: A Cia-funded data-mining juggernaut, http://www.forbes.com/sites/andy greenberg/2013/08/14/ agent-of-intelligence-how-a-deviant-philosopher-built-palantir-a-cia-funded-data-mining-juggernaut/, accessed 5 January 2016.

You can read more about Palantir at: https://www.palantir.com/ https://www.palantir.com/wp-assets/wp-content/uploads/2014/03/ Impact-Study-Fielding-an-Advanced-Analytic-Capability-in-a-War- Zone.pdf http://siliconangle.com/blog/2014/12/15/palantir-secures-first-60m- chunk-of-projected-400m-round-as-market-asks-who/ http://moneymorning.com/2015/07/28/as-palantir-ipo-date- approaches-heres-what-investors-need-to-know/ http://www.wsj.com/articles/SB100014240527023034978045792405010 78423362

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Data-Driven Product Innovation Summit June 29 & 30, 2016 | Austin, TX

Speakers Include

+1 415 614 4191 jc@theiegroup.com www.theinnovationenterprise.com

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How Do We Get Our Kids Interested In Tech?

Although the youngest generations are more reliant on tech than ever before, ever fewer of them actually show an interest in how it works. They are lightening fast to adopt the latest apps, but when their thoughts turn to actually creating the next generation, inspiration deserts them.

Science grads are down, maths grads are also in decline they don’t even have the teachers in schools to support their formative learning. As it stands, we won’t have the future intellectual firepower where we need it most.

Matthew Reaney Founder and Director, Big Cloud

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You could put children’s current ambivalence to tech down to a lack of ambition or intellectual curiosity, but I would personally put it down to lack of basic practical experience. A large project has just begun in the UK to remedy this: One million ‘micro:bits’ have been delivered by the BBC (the national television company) to 13-year-old schoolchildren in the UK. These programmable mini computers are filled with processors and sensors, and can be used by children with zero previous coding experience.

‘We wanted to try to create something that would ultimately help tackle the skills gap in the UK when it comes to the tech sector,’ said Ms Sinead Rocks, head of the BBC micro:bit project. ‘Children have many devices. They’re used to using tablets and smartphones.

We wanted to do something that transformed them from being passive users, to teach them something about what they use on a daily basis.’

If this initiative is supported by on-going teaching initiatives and curious parenting, it might just work.

When kids can hold something in their hands that shows them the cause and effect of their actions, it suddenly becomes real. For me, this is the starting point for an understanding of some of the more theoretical (but no less in-demand) career paths such as data science.

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For me, the jump from coding to my industry of data science is not such a big one. When kids get interested in analyzing patterns and testing hypotheses, the step to creating complex search strings is not such a difficult one. The key is retaining this interest through their schooling and university education, and even more of a challenge is attracting more women into the industry.

I believe that micro:bit and similar future schemes could prove to be a leveller to address the gender imbalance and also inspire kids with the power of creation rather than bore them with textbooks and theory. Primary schools around the world are starting to teach basic coding techniques ‘ something that develops a logical and inquisitive mind. When these techniques can be translated into real-world inventions, our next generation of tech founders will have the bug (excuse the pun).

As a parent, I wonder what sort of a world my two young children will grow up in. I will do my best to ensure that they are as technologically educated as possible - as this could well play a role in defining their future prosperity.

We all have a duty to get our kids interested in tech.

channels.theinnovationenterprise.com

What’s Changed Since The 2011 McKinsey Big Data Report?

George Hill, Editor-In-Chief

WE ARE NOW FIVE YEARS ON from the original predictions - five years that have seen big data become one of the most discussed subjects in society and the need for it in companies increasing exponentially.

So how accurate was McKinsey all those years ago? In their research, they offered seven key insights into big data and the future of big data, and we wanted to look at how they compared to what actually happened in the intervening years.

In 2011, McKinsey released their ‘Big data: The next frontier for innovation, competition, and productivity’ report, which outlined the ways big data was going to impact the world and the implications of the changes.

1

Data has swept into every industry and business function and is now an important factor of production This was an accurate prediction as data has now become adopted across practically every industry. Not only that, but it has been the catalyst for industries themselves and companies like Hortonworks have been valued in the billions. However, it is not necessarily as widely used as some may think as, according to a survey from the economist, 58% of companies are making limited progress in big data adoption.

However, there are some industries who have stormed ahead in this regard, with telecommunications having 62% of companies in an advanced stage, retailing with 68% and IT and technology with 57%.

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2

Big data creates value in several ways This prediction involved several different aspects, each of which has come to fruition, these include: Creating transparency We have seen through the opening up of government data and company records that big data has certainly created a more transparent society.

Enabling experimentation to discover needs, expose variability, and improve performance Again, this is certainly the case, we have seen that companies using data effectively report significant gains. Data has also allowed companies to make improvements in design, from Ford utilizing it to improve car design through to web designers using heat maps to optimize sites.

Segmenting populations to customize actions This was well underway when the report came out, with the prime example being the Democratic party using voter data effectively to target specific narrow demographics.

Replacing/supporting human decision making with automated algorithms AI and machine learning may not be at the level we know they will be, but the development has been rapid and is only going to speed up. We have seen almost every major tech company experimenting in this area.

Innovating new business models, products and services Data has allowed companies to find new markets, alterations to products and the creation of totally new products through the way that people use existing products today.

3

6

This has more or less been and gone, to the extent that the companies who jumped in early have found significant benefits and now those who don’t will always struggle to even enter many markets. There have been several court cases about the theft of data, with a prime example being when fitness tracker makers Jawbone sued their competitor Fitbit, as they poached staff members who then stole data from the company upon leaving.

To some extent this has occurred, but not to the levels that many had predicted. We have seen through the pure number and breadth of companies who we have been involved with that, although there is certainly a gap, it hasn’t really stopped companies taking advantage of data. Thanks to new consultancies, easy UX for data platforms and SaaS platforms, to some extent companies have alleviated the absolute need for the in-house data scientists that are lacking.

Use of big data will become a key basis of competition and growth for individual firms

4

The use of big data will underpin new waves of productivity growth and consumer surplus This point essentially discusses the indirect benefits of big data, which is hard to quantify but is certainly correct. One of the examples that is given is the use of traffic data being fed back to a user who then saves time on a journey, which is exactly what we have found with a number of traffic apps on phones and satnav units.

5

While the use of big data will matter across sectors, some sectors are poised for greater gains Once again, this is certainly correct and we have seen it with retail and financial services in particular. The reasons for both of these are varied, but one of the common elements is the transactional element of both businesses, making the use of data much simpler than more complex industries like manufacturing and healthcare. Due to this, they have become two of the top performing and strongest adopters, whilst others don’t have quite the same level of maturity.

There will be a shortage of talent necessary to take advantage of big data

7

Several issues will have to be addressed to capture the full potential of big data The healthcare industry is the prime example of this prediction coming true. The potential that data could have on the industry is huge, but until there is a way to effectively prevent the identification of individuals it simply isn’t possible.

However, the largest area of improvement needed is simply to get people to trust companies and governments with their data. We saw with the recent Safeharbor changes that even the courts don’t believe enough is being done to protect data from the US government and the theft of data from Ashley Madison saw the destruction of hundreds of thousands of lives. Until the issue of effective protection and safeguarding is addressed, it will be impossible for the full potential to be realized.

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Big Data & Analytics for Pharma Summit

June 9 & 10, 2016 | Philadelphia Speakers Include

+1 415 614 4191 jc@theiegroup.com www.theinnovationenterprise.com

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